Work and Non-Work Sickness Presenteeism
Bibliographic record
Abstract
OBJECTIVE: To test the role of workplace coronavirus disease (COVID-19) climate in shaping employee attitudes toward the CDC prevention guidelines and subsequent levels of work and non-work sickness presenteeism. METHODS: Three waves of anonymous survey data were collected in October and December 2020 and February 2021. Participants were 304 employed adults in the U.S., of whom half were working onsite. RESULTS: Time 1 workplace COVID-19 climate was positively associated with Time 2 employee attitudes toward the CDC prevention guidelines, which in turn predicted Time 3 levels of non-work and work sickness presenteeism. CONCLUSIONS: The workplace can shape employee attitudes toward the CDC COVID-19 prevention guidelines and their work and non-work sickness presenteeism, thus highlighting the important role companies have in reducing community spread of the novel coronavirus in work and non-work settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".